Senior Staff Applied Scientist, Time Series & Demand Forecasting

We are seeking a Senior Staff Applied Scientist with deep expertise in time-series and demand forecasting to set the scientific direction for our forecasting platform. This is a hands-on senior individual-contributor role with no direct reports. You will define the science, establish technical standards, and guide multiple engineering and data-science teams to deliver the roadmap.

You will own the scientific strategy and measurement framework used to improve forecasting accuracy, robustness, and credibility. You will also influence long-term technical direction as the platform scales to millions of series and daily forecasting.

Vacancy Status

This is an existing job vacancy

Our AI team develops machine-learning solutions across forecasting, segmentation, explainability, and optimization for industries including Consumer Packaged Goods, Retail, and Life Sciences. We combine rigorous science with real business problems, and our work is deployed to enterprise customers worldwide.

Demand forecasting is central to the Kinaxis platform. We are building a dedicated data-science and AI research engine to deliver forecasting that is accurate, self-calibrating, explainable, and cost-effective at enterprise scale.

• Set the forecasting science agenda. Define the research roadmap and translate complex business goals into clear ML problems, measurable success criteria, and technical priorities.
• Lead through influence. Provide technical direction, mentor senior scientists and ML engineers, review designs, and strengthen scientific judgment across teams.
• Advance forecasting capabilities. Guide evaluation and development across classical time-series, machine- learning, probabilistic, and pretrained forecasting approaches, including hierarchical and intermittent demand, covariates, cold starts, segmentation, explainability, and domain adaptation.
• Establish scientific rigor. Define accuracy metrics, benchmarks, quality gates, backtesting standards, and reproducible experiment-to-validation practices.
• Shape architecture for scale. Partner with architects, MLOps, and platform and data-infrastructure teams to ensure solutions can support millions of series and daily forecasting.
• Communicate and align. Explain scientific trade-offs to technical and executive audiences and build alignment across accuracy, scalability, cost, operational simplicity, and commercial priorities.
• What Success Looks Like
• Within six months, a fixed and reproducible accuracy benchmark is in place, teams report against it, and the research roadmap is aligned with engineering and product leadership.